Superpixel Segmentation: An Evaluation
David Stutz · Lecture notes in computer science · 2015
In recent years, superpixel algorithms have become a standard tool in computer vision and many approaches have been proposed. However, different evaluation methodologies make direct comparison difficult. We address this shortcoming with a thorough and fair comparison of thirteen state-of-the-art superpixel algorithms. To include algorithms utilizing depth information we present results on both the Berkeley Segmentation Dataset [ 3 ] and the NYU Depth Dataset [ 19 ]. Based on qualitative and quantitative aspects, our work allows to guide algorithm selection by identifying important quality characteristics.